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Reliable Machine Learning

Type Hinting

Explore how Python type hinting enhances ML software reliability by reducing runtime errors and improving code clarity. Learn important typing concepts like List, Dict, Union, and Optional, and use the mypy tool to verify type correctness and prevent mismatches, helping you write robust, maintainable ML code.

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Overview

Strong static typing catches many errors that can be missed in a dynamic typing environment. Python’s type hinting can slightly reduce the need for runtime checks. It also improves code understanding for other engineers.

Let’s consider an example below. First, we’ll execute the code below to see if it works successfully or not with the broken type annotation.

Python
from typing import List
def sum_odds(numbers: List[int]):
    return sum(x for x in numbers if x % 2)
print(sum_odds([1, 2., 3, 4]))

The above example works fine, but its type annotation is broken. Let's verify it by running the same code with the mypy command.

Python
from typing import List
def sum_odds(numbers: List[int]):
    return sum(x for x in numbers if x % 2)
print(sum_odds([1, 2., 3, 4]))

Python’s typing

For those who haven’t worked with typing in ...